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"The face of the operation is Briatore (referred to exclusively in the film by his colleagues and angry, chanting detractors as "Flavio"), an anthropomorphic radish who spends most of his time at QPR plotting to fire all of the managers."

At press time, Harbaugh had sent Michigan’s athletic department an envelope containing a heavily annotated seating chart, a list of the 63,000 seat views he had found unsatisfactory, and a glowing 70-page report on section 25, row 12, seat 9, which he claimed is “exactly what the great sport of football is all about.”

Ranking Defenses Based on Difference Compared to Average

Offense and defense rankings based on total numbers and straight averages can be misleading at times. If a team plays opponents with strong rush offense but weak pass offense, the team's pass defense stats might look better than what they really should be. This is something Michigan was being accused of due to the fact that much of our "bad" defensive games came against strong rushing teams (Alabama and Air Force).

One way to mitigate this "effect" would be to not look at the totals and average numbers, but compare the game output against the average output the opponent has produced against all opponents. This produces numbers that show you how good your performance was compared to all other team that your opponent has played. It is more useful comparative method than using just total numbers.

So, exactly how does it work?

Here are the stats for Michigan so far this year:

Opponents

Rush Net Total

Pass Yds Total

Total Yds

Pts

Avg Rush Total

Avg Pass Total

Avg Total Offense

Avg Scoring Offense

Alabama

232

199

431

41

214.38

222

436.38

40.63

Air Force

290

127

417

25

366.25

114.38

480.63

34.5

Massachusetts

112

147

259

13

101.375

169.125

270.5

11.25

Notre Dame

94

145

239

13

196.5

193.25

389.75

26.38

Purdue

56

162

218

13

161.25

220.25

381.5

30.88

Illinois

105

29

134

0

132.5

184.88

317.38

18

Michigan St.

112

192

304

10

131.22

229.11

360.33

19.22

Nebraska

160

166

326

23

264.13

225

489.13

39.25

Average All Opp

145.1

145.9

291.0

17.3

196.0

194.7

390.7

27.5

Opponents

Avg Rush Off Diff

Avg Pass Off Diff

Avg Total Off Diff

Avg Scoring Off Diff

Alabama

8%

-10%

-1%

1%

Air Force

-21%

11%

-13%

-28%

Massachusetts

10%

-13%

-4%

16%

Notre Dame

-52%

-25%

-39%

-51%

Purdue

-65%

-26%

-43%

-58%

Illinois

-21%

-84%

-58%

-100%

Michigan St.

-15%

-16%

-16%

-48%

Nebraska

-39%

-26%

-33%

-41%

Average All Opp

-24%

-24%

-26%

-39%

The first four columns of stats represent the actual stats from the game played against Michigan. The second set (of four) columns are the average output of that team against all opponents this year. The last set (of four) columns second table are the differences in percentage of actual game stat versus the total year averages.

As you can see from the table, Alabama produced their average offensive output against Michigan while Purdue and Illinois barely produced about half of their normal offensive output.

By averaging all of the averages, we find that our defense is reducing our opponents' normal offensive output by about 25%, while only allowing only 61% of their normal scoring output.

Sounds pretty good, but how does that compare to rest of NCAA?

I didn't have enough time to calculate the differential averages for every team in NCAA, but I did the analysis for top 10 Pass/Rush/Total defensive teams and all of Big Ten (plus ND). I did not include stats against FCS opponents. Here it is ranked by total offense differential.

Rk

School

Avg Rush
Off Diff

Avg Pass
Off Diff

Avg Total
Off Diff

Avg Scoring
Off Diff

1

Alabama

-64%

-34%

-49%

-71%

2

LSU

-50%

-30%

-37%

-45%

3

Florida St.

-34%

-36%

-36%

-51%

4

BYU

-45%

-29%

-31%

-44%

5

Michigan St.

-50%

-15%

-30%

-46%

6

Michigan

-24%

-24%

-26%

-39%

7

Notre Dame

-56%

-15%

-25%

-65%

8

Connecticut

-18%

-24%

-23%

-24%

9

Wisconsin

-29%

-17%

-21%

-31%

10

Maryland

-34%

-11%

-21%

-11%

11

Bowling Green

-32%

-15%

-18%

-39%

12

Boise St.

-15%

-19%

-16%

-44%

13

Stanford

-62%

3%

-15%

-37%

14

Oregon St.

-39%

4%

-15%

-39%

15

Nebraska

-1%

-24%

-14%

-7%

16

Fresno St.

-14%

-18%

-14%

-24%

17

Arizona St.

5%

-32%

-14%

-17%

18

Rutgers

-41%

-5%

-13%

-39%

19

Penn St.

-39%

9%

-13%

-35%

20

Minnesota

16%

-30%

-9%

-1%

21

Iowa

-1%

-17%

-8%

-20%

22

Illinois

-12%

-4%

-6%

5%

23

Ohio St.

-31%

9%

-5%

-20%

24

Vanderbilt

14%

-16%

-1%

-18%

25

Northwestern

-19%

13%

1%

-13%

26

Purdue

14%

6%

11%

15%

27

Indiana

17%

17%

18%

19%

Few things that stand out:

Alabama, LSU, and Florida St defense stand above the rest

Michigan and Michigan St defenses stand above the rest of B1G

Michigan is pretty good at both run and pass defense

Ohio St pass defense is HORRIBLE!

BYU defense is much better than I thought

Many of the defenses highly ranked in one (pass or rush) only because they are so horrible at the other (I am looking at you Arizona St, Stanford, Nebraska and Oregon St!)

Notre Dame is living on borrowed time - their scoring differential is MUCH higher than what rest of the defensive differentials would indicate

I do believe converting straight up numbers to percentages makes it much easier to compare between pass/rush and between different teams. I hope most of you find this useful. If I get enough upvotes, I will do the same analysis for offense as well.

Nice work, a definite upvote. While looking at the data, I wondered about the effect both defensive turnovers created and turnover margin might have on the yardage stats as another way to say that good defenses force turnovers and how that might relate to the differences in terms of overall yardage pct. vs scoring diff pct.

I added the data here, I think this more likely explains what you refer to as ND living on borrowed time......

I love the analysis. I think it is a much better indicator of a defenses play than the straight numbers.

The only major caveat I see to the numbers is a problem in comparing any college football stats lately and that is the tempo of the team. Arizona and Oregon are never going to have top defensive stats because they generate so many plays in a game they create more possessions. More possessions= more yards and more points. I know Ohio St was trying to be more up tempo this year, but I'm not sure if they have been successful in that, but that could be a factor in their horrid stats.

My solution would be taking the avg yards per play and then comparing it to their avg yards per play against the seasons average. I think this would give you a more accurate number. Something to think about. Great work though I loved looking at your numbers.

Actually, I think your concern is exactly what this analysis is addressing. This model de-emphasizes actual amount of yards.

Take Oregon for example. They average something like 550 yards of offense every game. If Michigan played them and allowed 400 yards of total offense, our rank woud go up even though we just allowed double the number of yardages compared to Illinois since, percentage wise, we did little better than expected.

He thinks that this would underrate OREGON's defense, not their opponents' defense. Because on average their opponents get more possessions than usual and should generally get more yards and score more points.

If we believe Oregon D is at disadvantage because of additional snaps, I think we would see the biggest effect on the pass defense numbers as the opposing team would have to turn to the air after they fall behind. But that is not what we see here, Oregon's rush D is pretty awful. I don't care how many snaps Arkansas St gets, they should NEVER gain 226 yards on the ground when they barely average over 100 yards against all other opponents.

The Oregon's pass defense in contrast is one of the better ones in Pac 12.

I don't think we are seeing a very significant impact of added snaps on these percentages.

This is obviously better than the straight numbers, but the problem with this approach is that different teams schedules can have very different levels of difficulty. For example, if team A plays a very difficult schedule, they're not going to change those strong teams offensive output as much as team B, who plays nobody but cupcakes.

Models are made so we can review some data and have an intelligent discussion. If you want to know everything and take everything into account including scheduling strength differences you can go see that kind of analysis with the numbers from USA Today and Sagarin.....